{"id":"W1892021512","doi":"10.1002/jwmg.991","title":"Evaluating sources of censoring and truncation in telemetry‐based survival data","year":2015,"lang":"en","type":"article","venue":"Journal of Wildlife Management","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Environment and Protected Areas","funders":"Calgary Institute for the Humanities, University of Calgary; Natural Sciences and Engineering Research Council of Canada; University of Alberta; Alberta Conservation Association; World Wildlife Fund; Humanities Montana; Weyerhaeuser Company","keywords":"Censoring (clinical trials); Statistics; Woodland caribou; Survival analysis; Demography; Econometrics; Biology; Ecology; Mathematics; Predation","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.08042053,0.0004860416,0.000710187,0.001589567,0.001086046,0.002048408,0.001941708,0.001250501,0.001266261],"category_scores_gemma":[0.2308641,0.0004643597,0.001619856,0.001962035,0.001787837,0.001465411,0.00165415,0.001138076,0.0001719802],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003063172,"about_ca_system_score_gemma":0.002385346,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02280283,"about_ca_topic_score_gemma":0.02243939,"domain_scores_codex":[0.9664765,0.02417439,0.002130661,0.002246664,0.004332543,0.0006392696],"domain_scores_gemma":[0.5518628,0.3744706,0.03789026,0.02361712,0.01090073,0.001258456],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008010573,0.00008782535,0.7558071,0.0003183361,0.001554319,0.0003844327,0.001355444,0.1720391,0.001236606,0.008961104,0.001049458,0.05640512],"study_design_scores_gemma":[0.0001280271,0.0006080979,0.4101314,0.0006946789,0.0009822156,0.0007609691,0.001112659,0.5457689,0.005999436,0.02937099,0.004233279,0.0002093634],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7429595,0.001069862,0.2504647,0.0006984482,0.00009628904,0.0003163859,0.001101181,0.0003643137,0.002929341],"genre_scores_gemma":[0.9771569,0.0001132537,0.02144991,0.0001283547,0.000020692,0.0001015433,0.0006443609,0.0000307982,0.0003540656],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08042053,"threshold_uncertainty_score":0.4253095,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1081766748653318,"score_gpt":0.330393587913641,"score_spread":0.2222169130483092,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}